Trading-strategy-back-tester

QR码作为一种可高速、全方位识别的二维条码,已经在各行各业中得到了应用,并具有很大发展潜力.本文针对QR码图像的识别过程中可能遇到的图像噪声干扰、QR码占实际拍摄图像的一部分、倾斜、几何失真等问题进行了研究,提出了通过改进型中值滤波去除噪声,使用QR码符号特性定位并对倾斜图像进行旋转,对失真不严重的图像采用控制点变换和双线性插值方法进行几何校正.实验证明, 本文的方法简单有效, 可以用于提高条码的正确识别率.(As a high-speed, full identification of the two-dimensional bar code, QR code has been applied in all walks of life, and also has great potential for development. In this paper, aiming at QR code image recognition process may be encountered in noise interference, QR code accounting for the actual captured image just a part, slant, geometric distortion, and other issues have been studied, put forward through improved median filter to remove noise, use QR code symbol characteristics to locate and rotate the tilted image, distortion which is not serious use control-point transform and spatial bilinear interpolation method for geometric correction. The results show that this method is simple and effective, can be used to improve the QR code correct recognition rate.)

发动机系统仿真，matlab/simulink 系统仿真和运算，可以根据油门估算出扭矩(Engine system simulation, matlab/simulink simulation and computing systems, torque can be estimated based on throttle)

导航对准中的传递传递对准，采用角速度匹配。(Transfer alignment in navigation alignment, using angular velocity matching.)

matlab for images processing

里面是Matlab宝典丛书的源代码，应该来说对于学习Matlab的人员是很有用的(Matlab Books Collection which is the source, should be run for staff learning Matlab is useful)

基于matlab/simulink的反激变换器仿真(Based on matlab/simulink simulation flyback converter)

Read table from HTML file (url) to Matlab cell array.

说明：  最小均方算法的仿真， LMS算法仿真在移动通信的应用(Least mean square algorithm simulation, LMS algorithm for the application of simulation in mobile communications)

用matlab编辑的一个读入图片代码，读入时需将图片放在指定位置。(Edited by matlab a picture of the code to read, to be read in the picture in the specified location.)

标准压缩感知（CS）理论决定了可靠的信号恢复是可能给M= O（KLOG（N / K））的测量。我们证明了它可以通过利用超越简单的稀疏性和可压缩性由包括价值观和信号系数的位置之间的依赖关系更加逼真信号模型大大降低Mwithout牺牲的鲁棒性。(The standard compressive sensing (CS) theory dictates that robust signal recovery is  possible from  M=O(Klog(N/K))  measurements. We demonstrate that it is possible to substantially  decrease  Mwithout sacrificing robustness by leveraging more  realistic signal models that go beyond simple sparsity and  compressibility by including dependencies between values and  locations of the signal coefficients.   )